How Does Bilateral Ear Symmetry Affect Deep Ear Features?
This work addresses a specific gap in biometric ear recognition for improving accuracy, but it is incremental as it builds on existing CNN methods by incorporating side information.
The paper tackled the problem of how bilateral ear symmetry affects CNN-based ear recognition by developing an ear side classifier and testing its impact on performance, finding that treating left and right ears separately leads to notable improvements in verification rates across five datasets.
Ear recognition has gained attention as a reliable biometric technique due to the distinctive characteristics of human ears. With the increasing availability of large-scale datasets, convolutional neural networks (CNNs) have been widely adopted to learn features directly from raw ear images, outperforming traditional hand-crafted methods. However, the effect of bilateral ear symmetry on the features learned by CNNs has received little attention in recent studies. In this paper, we investigate how bilateral ear symmetry influences the effectiveness of CNN-based ear recognition. To this end, we first develop an ear side classifier to automatically categorize ear images as either left or right. We then explore the impact of incorporating this side information during both training and test. Cross-dataset evaluations are conducted on five datasets. Our results suggest that treating left and right ears separately during training and testing can lead to notable performance improvements. Furthermore, our ablation studies on alignment strategies, input sizes, and various hyperparameter settings provide practical insights into training CNN-based ear recognition systems on large-scale datasets to achieve higher verification rates.